Analysis of newly established EST databases reveals similarities between heart regeneration in newt and fish
© Borchardt et al; licensee BioMed Central Ltd. 2010
Received: 12 June 2009
Accepted: 4 January 2010
Published: 4 January 2010
The newt Notophthalmus viridescens possesses the remarkable ability to respond to cardiac damage by formation of new myocardial tissue. Surprisingly little is known about changes in gene activities that occur during the course of regeneration. To begin to decipher the molecular processes, that underlie restoration of functional cardiac tissue, we generated an EST database from regenerating newt hearts and compared the transcriptional profile of selected candidates with genes deregulated during zebrafish heart regeneration.
A cDNA library of 100,000 cDNA clones was generated from newt hearts 14 days after ventricular injury. Sequencing of 11520 cDNA clones resulted in 2894 assembled contigs. BLAST searches revealed 1695 sequences with potential homology to sequences from the NCBI database. BLAST searches to TrEMBL and Swiss-Prot databases assigned 1116 proteins to Gene Ontology terms. We also identified a relatively large set of 174 ORFs, which are likely to be unique for urodele amphibians. Expression analysis of newt-zebrafish homologues confirmed the deregulation of selected genes during heart regeneration. Sequences, BLAST results and GO annotations were visualized in a relational web based database followed by grouping of identified proteins into clusters of GO Terms. Comparison of data from regenerating zebrafish hearts identified biological processes, which were uniformly overrepresented during cardiac regeneration in newt and zebrafish.
We concluded that heart regeneration in newts and zebrafish led to the activation of similar sets of genes, which suggests that heart regeneration in both species might follow similar principles. The design of the newly established newt EST database allows identification of molecular pathways important for heart regeneration.
Some urodele amphibians possess an extraordinary capacity for regeneration [1, 2]. The newt Notophthalmus viridescens (order caudata, family Salamandridae, genus Notophthalmus) can completely regenerate limbs, tail, jaws, lens and retina after amputation or following attacks of natural predators. Interestingly, newts are also able to regenerate internal organs such as parts of the central nervous system  and parts of the ventricles of the heart after amputation or mechanical damage [4–6] suggesting that they exhibit a general program that enables regenerative responses. This ability distinguishes newts (and also teleost fish) from other vertebrates, which have lost (or supress) the capacity for comprehensive organ regeneration. Mammals, for example, are not able to repair substantial cardiac injuries by replacement with functional tissue. Instead, the mammalian heart responds with scar formation and fibrosis resulting in severe functional impairment (reviewed in [7, 8]). The failure of mammalian hearts to regenerate seems to be caused by the inability of cardiac myocytes to proliferate and a preference for hemostasis and fibrosis, which occurred during evolution, together with the advent of high-pressure circulation [9, 10].
Newt cardiomyocytes are virtually non-proliferative in undamaged hearts but respond to cardiac injuries by proliferation [4, 5, 11, 12]. At present, it is not clear whether proliferating cardiomyocytes arise solely from resting cardiomyocytes, which might undergo partial de-differentiation or re-programming, or also from dedicated cardiac stem cells that are able to replace lost cardiac tissue. Despite this open question it is undisputed that newt cardiomyocytes re-enter the cell cycle in inductive environments. The plasticity of newt cardiomyocytes to adopt a different cellular fate is also demonstrated by their ability to contribute to skeletal muscle formation in regenerating limbs .
Understanding gene expression during critical stages of cardiac regeneration is a fundamental prerequisite to decode the molecular processes, which enable newt cardiomyocytes to re-enter the cell cycle and functionally replace, lost cardiac tissue. Considering its exceptional regenerative capabilities, surprising little sequence information is available for the newt. By the end of 2008, 125 entries were listed in the NCBI database, which encode a maximum of 83 non-redundant proteins including 5 very short sequences. Another 12 unique sequences were present in the nucleotide database resulting in a total number of around 100 unique sequences, which correspond to expressed genes from N. viridescens. Limited EST datasets exist so far for two other urodele amphibians; the Mexican axolotl (Ambystoma mexicanum) and the Eastern tiger salamander (A. tigrinum tigrinum) [14, 15]. Both species belong to the family of Ambystomatidae (mole salamander) that have separated from the Salamandridae family after divergence of family-level Salamander lineages more than 150 million years ago [16, 17]. The paucity of annotated sequence information for newts severely compromises efforts to analyze changes in the transcriptional profile during regeneration. This is emphasized by a recent attempt to characterize transcriptional changes during nerve dependent limb regeneration. Due to the low degree of sequence annotation, the combination of microarray and 454 sequencing analysis yielded only a limited number of new contigs after assembly of 454 cDNA sequences with existing expressed sequence tags (ESTs) even though the number of non-redundant human-A. mexicanum orthologous sequences was increased considerably .
The size of the newt genome, archived on 22 chromosomes is estimated to be around 10 times larger than most mammalian genomes [19, 20] which impedes attempts to determine the complete genomic sequence. We therefore decided to concentrate on expressed sequences. This reduces the complexity of sequence information that needs to be analyzed but allows a comprehensive view on genes expressed during cardiac regeneration in the newt. In this study, we focused on newt hearts 14 days after damage, the time when highest cell proliferation during heart regeneration occurs [4, 11]. We have assembled and annotated a large set of sequence data resulting in the identification of a group of potential open reading frames, which seem to be unique for urodele amphibians. Furthermore, a web based relational database was constructed that combines sequence data and functional annotations. The database was used to compare transcriptional signatures of damaged hearts of newts and zebrafish, which is the only other known vertebrate capable of cardiac regeneration.
Results and Discussion
Analysis of EST quality and contig assembly
We assembled the 9696 ESTs into 2894 contigs with the commercial software seqman™ Pro using default parameters. The average contig length was determined to be 627 bp. Most contigs were in the range of 900 bp (Figure S2a) in additional file 1). Since the newt heart library was not normalized, we quantified the EST distribution per contig to determine the redundancy level of our EST dataset. 2000 ESTs (69.1%) were only present as singleton sequences, whilst 387 contigs (13.3%) contained only two EST clones, a ratio that is very similar to the EST sequencing projects of A. mexicanum [14, 15]. On average, the number of ESTs per contig was 3.1, which confirmed the high diversity of our dataset. 130 contigs, assembled from 3949 sequence reads, contained more than 20 ESTs (Figure S2b in additional file 1), most probably representing transcripts with highest abundance at this stage of heart regeneration.
BLAST searches, homology assignment, and EST annotation
To identify putative homologies to known nucleotide and protein sequences, we performed separate BLASTn, BLASTx and tBLASTx searches for the 2894 assembled contigs in NCBI nr nucleotide and EST databases. 1695 contigs with a BLAST score of at least = 1e-05 were used for further analyses. According to the BLAST scores 404 contigs were categorized as weakly similar (≤ e-05 > 1e-20) and the remaining 1291 contigs in significantly similar (< 1e-20) to existing entries in the NCBI database (Figure 1). To further analyze which species showed the highest similarities for the individual contigs, we extracted BLAST hits with the smallest e-values from the different BLAST runs (Table S1 in additional file 2). Not surprisingly, 66.66% of all best hits were assigned to amphibians with the majority belonging to 15 identified salamander species, followed by 15.93% of best hits for 19 different mammalian species and 10.86% of best hits for 5 different avian species (Table S1 in additional file 2). Most of the best hits corresponded to EST sequences, which lack functional annotations or protein identities. However, BLASTx searches revealed significant similarities for many of these contigs to known protein entries in NCBI. Thus, for submission to GenBank all initial 2894 contigs were split into their respective ESTs and deposited together with the information about the best hits (score ≤ 1e-05) from all BLAST algorithms applied (Figure 1). All 9696 EST sequences were published in GenBank on July 30, 2009 under the accession numbers GO925352 to GO935047
Analysis of contig sequences
Full-length contig analysis of BLAST hits with an E-value < 1-05.
alignment start amino acid position
number of contigs
number of contigs full length (polyA)
Contigs full length total
Gene families with highest abundance in the newt heart EST library.
Number of ESTs
Number of contigs
Abundance (%) total
beta-globin, minor adult alpha-globin, Hemoglobin beta-2 subunit, major adult alpha-globin chain
Paramesotriton hongkongensis mitochondrion, Notophthalmus viridescens voucher UTA 56597 cytochrome b (cytb) gene, partial cds; mitochondrial, Notophthalmus viridescens NADH dehydrogenase subunit I (ND1) gene, partial cds; tRNA-Ile, tRNA-Gln, and tRNA-Met genes, complete sequence; NADH dehydrogenase subunit II (ND2) gene, complete cds; tRNA-Trp tRNA-Ala, tRNA-Asn, tRNA-Cys, and tRNA-Tyr genes, complete sequence; and cytochrome c oxidase subunit I (COI) gene, partial cds; mitochondrial genes for mitochondrial products
X. laevis mRNA for myosin light chain, Cynops ensicauda b-actin mRNA for beta actin, Ambystoma mexicanum tropomyosin mRNA, Sus scrofa MyHC-slow mRNA for myosin heavy chain slow isoform, Xenopus laevis myosin light chain 1av (MLC1av) mRNA, HSTNCS Human mRNA for slow skeletal troponin C (TnC), Bufo marinus cardiac troponin I (Tnni3) mRNA
Xenopus laevis similar to ribosomal protein S20, mRNA (cDNA clone MGC:52591 IMAGE:5542932), complete cds; Ribosomal protein L23 [Mus musculus] gb|AAH10114.1; Ictalurus punctatus ribosomal protein L36 mRNA, complete cds; Xenopus tropicalis 40S ribosomal protein S6, mRNA (cDNA clone MGC:76307 IMAGE:5379757), complete cds;Latimeria chalumnae ribosomal protein L7 mRNA, partial cds; Xenopus tropicalis 60S ribosomal protein L35, mRNA (cDNA clone MGC:89716 IMAGE:7026875), complete cds
Matches of newt heart ESTs to existing NCBInr entries for N. viridescens.
Match to con.
NADH dehydrogenase subunit 1 [Notophthalmus viridescens]
NADH dehydrogenase subunit 1 [Notophthalmus viridescens]
NADH dehydrogenase subunit 2 [Notophthalmus viridescens]
NADH dehydrogenase subunit 2 [Notophthalmus viridescens]
cytochrome c oxidase subunit 1 [Notophthalmus viridescens]
cytochrome c oxidase subunit 1 [Notophthalmus viridescens]
cytochrome oxidase subunit I [Notophthalmus viridescens]
cytochrome c oxidase subunit 2 [Notophthalmus viridescens]
< 1e-150< 1e-1501e-104
ATP synthase F0 subunit 8 [Notophthalmus viridescens]
ATP synthase F0 subunit 6 [Notophthalmus viridescens]
cytochrome c oxidase subunit 3 [Notophthalmus viridescens]
< 1e-150< 1e-150
NADH dehydrogenase subunit 3 [Notophthalmus viridescens]
NADH dehydrogenase subunit 4 [Notophthalmus viridescens]
NADH dehydrogenase subunit 4 [Notophthalmus viridescens]
NADH dehydrogenase subunit 5 [Notophthalmus viridescens]
NADH dehydrogenase subunit 6 [Notophthalmus viridescens]
cytochrome b [Notophthalmus viridescens]
cytochrome b [Notophthalmus viridescens]
cytochrome b [Notophthalmus viridescens]
cytochrome b [Notophthalmus viridescens]
histone H3a [Notophthalmus viridescens]
Newt histone H4 gene, partial
Tubulin alpha chain
< 1e-150< 1e-1502e-029
alpha-tubulin [Notophthalmus viridescens]
< 1e-150< 1e-1502e-029
cytokeratin type II [Notophthalmus viridescens]
cytokeratin 8 [Notophthalmus viridescens]
complement component C3 [Notophthalmus viridescens]
tissue inhibitor of metalloproteinase 1 [N. viridescens]
Functional annotation of assembled newt contigs to Gene Ontology terms
Distribution of GO annotated protein identifiers to different animal classes.
Comparison of transcriptional changes during newt and zebrafish heart regeneration
List of identical protein identifiers from deregulated zebrafish proteins and newt proteins annotated to TrEMBL and SwissProt databases.
Identification of biological pathways activated during zebrafish and newt heart regeneration by GO annotation
So far the analysis indicated that our EST dataset is enriched in sequences, which were modulated during newt heart regeneration, and that such sequences were also often expressed at different levels during regeneration of zebrafish hearts. We next asked whether we could identify common transcriptional pathways or signaling cascades, in which annotated newt proteins accumulate. We assumed that deregulated zebrafish ESTs represent a subset of genes, which mark regenerative processes. All newt and zebrafish proteins that were previously assigned to a GO term within the root node "biological process" (1043 and 244 protein sequences, respectively) were included in our search for genes involved in regeneration. We then selected a number of daughter nodes that cover processes, which are most likely involved in tissue regeneration. The following nodes were selected: 1.) "Embryonic or larval gene expression programs" since regeneration might require the re-initiation of early embryological processes which are needed to build tissues or organs. 2.) " Wound healing and cell migration", since the early phase of heart regeneration in zebrafish and newt involves recruitment and migration of inflammatory cells into the damaged tissue and other typical wound healing processes [4, 23]. 3.) "Cell proliferation and cell death", since remodeling of damaged tissue requires proliferation of cells and removal of non-functional cell types within the damaged heart. 4.) "Cell differentiation, cell division and cell cycle", since new functional cells have to be generated by cellular differentiation of precursor cells that might originate from de-differentiated, reprogrammed cardiomyocytes [2, 13, 26] or from stem cells residing within the heart . 5.) "Circulation and growth of tissue", since epicardial cells migrate into the wound area of zebrafish hearts and contribute to the establishment of functional cardiac tissue by formation of microcapillaries . 6.) "Muscle contraction", since reprogramming of existing newt cardiomyocytes results in down regulation of mature cardiomyocyte markers and expression of smooth muscle proteins . 7.) "Cell surface receptor linked signal transduction and intracellular receptor mediated signaling pathways", since signaling events, which originate at the cell membrane, will contribute to the initiation and regulation of cardiac regeneration.
We first generated a tree diagram displaying all possible GO term daughter nodes derived from the above selected ancestor terms using the freeware tool BLAST2GO  (Figure S3 in Additional file 4). Next, proteins were directly annotated to their respective GO nodes and then annotated to their connected parental nodes until the root biological process was reached. This allowed us to functionally connect proteins that are otherwise not assigned to the same specific GO term. 599 protein identifiers were directly associated to 331 different GO terms in newts (Table 3 in additional file 5). For the zebrafish dataset we associated 192 protein identifiers to 197 GO terms (Table S3 in additional file 5). The largest number of protein identifiers (almost one third of all annotated newt proteins and 30% of all zebrafish proteins) was related to a GO term connected to development. A clear difference in protein numbers between newt and zebrafish was detected for the GO ancestor term "intracellular receptor mediated signal", which is most probably due to relative small numbers of proteins associated with this term in the zebrafish dataset. Interestingly, we did not detect any protein identifier for the ancestor term "antigen receptor-mediated signaling pathway" in zebrafish and only 6 proteins within the newt database suggesting that cellular immune response may be of minor relevance at these stages of heart regeneration.
The total number of proteins within a dataset that has been assigned to a given GO term does not necessarily reflect the relative distribution of proteins between different terms, since the number of proteins that are covered by a given term might differ. Therefore, we compared the total number of proteins annotated to the GO term biological process to the total number of proteins in selected GO term nodes. This procedure allowed us to determine the ratio of proteins annotated to the GO term nodes GO:0007275: development, GO:0042060: wound healing, GO:0016477:cell migration, GO:0008283: cell proliferation, GO:0008219: cell death, GO:0030154: cell differentiation, GO:0051301: cell division, GO:0007049: cell cycle, GO:0000278: mitotic cell cycle, GO:0008015: circulation, GO:0040007: growth, GO:0006936: muscle contraction and GO:0007166: cell surface receptor linked signal transduction. To verify an accumulation of proteins in a given ancestor term, we also compared the ratio of proteins annotated to a given GO term to the total number of proteins available from the GOA database, which was used as an unbiased standard dataset (Table S3 in additional file 5).
Identification and analysis of genes associated with highly enriched GO term sub nodes
To prove that enrichment analysis of GO terms allows a more efficient identification of deregulated candidate genes that might be involved in the regeneration process rather than random selection, we performed statistical analysis for all 331 GO terms from the newt dataset based on chi square test. In the course of the analysis we made a one-to-one comparison for the GOA and newt datasets. The p-values represent the level of significance by which our observations are independent from each other. For the above-mentioned GO terms, the detected enrichment was statistically significant (Table S4 in additional file 6).
To learn more about the expression kinetics of individual genes which were present in highly enriched GO terms (>10-fold), we performed a RT-PCR analysis using tissues from hearts isolated at four different time points during heart regeneration. Four genes were selected that were both identified in newt and zebrafish hearts: 1.) Karyopherin or importin alpha2 (kpna2, importin alpha-2), which is involved in the first step of nuclear protein import and is believed to be regulated in a cell cycle dependent manner . Karyopherin is regulated by TGF beta1 and IFN-gamma mediated signaling  amongst many other pathways. 2.) Survivin (Baculoviral IAP repeat-containing protein, BIRC5), which is member of a family of inhibitors of apoptosis and functions as a key regulator of mitotic cell cycle and programmed cell death . Survivin is a target of the PI3K/Akt Pathway  and interacts in a cell cycle dependent manner with the small GTPase Ran . 3.) Clusterin (Apolipoprotein J, Apo-J), which is a glycoprotein expressed as an intracellular isoform, and as a secreted protein. The nuclear form of clusterin (nCLU) is pro-apoptotic while the secretory form (sCLU) has a pro-survival role [33, 34]. Clusterin has been implicated in various cell functions involved in carcinogenesis and tumor progression . 4.) Cyclophilin A (Peptidyl prolyl isomerase A, PPIA), which is a member of the peptidyl-prolyl cis-trans isomerase (PPIase) family involved in cyclosporin A mediated immunosuppression [36, 37]. In H1299 lung cancer cells, down regulation of Cyclophilin A prevents up regulation of Cyclin D1 and cdk4 expression for subsequent cell cycle progression . The expression level of all 4 selected genes differed from normal hearts at all 4 time points analyzed. The highest expression was detected at 21 days after injury. An almost two fold change was detected for survivin, which was down regulated to less than 60% of normal levels 4 days after newt heart damage, although changes in expression levels were statistically not significant (data not shown). The expression of survivin in zebrafish was up regulated with expression levels that were highest at 3 and 7 days after amputation. This illustrates differences during newt and zebrafish heart regeneration despite multiple similarities.
To ask whether our GO term based functional screen is able to identify additional transcripts that may change their expression level during newt heart regeneration, we selected candidate proteins from GO terms, which showed a more than 10 fold accumulation ratio. Candidates were chosen which either did not undergo a change in expression levels during zebrafish heart regeneration or which were not present on zebrafish Affymetrix arrays. For transcripts associated to "cell cycle regulation" we included the GTP-binding nuclear protein Ran, which is required for nucleo-cytoplasmic shuttling of proteins and control of DNA synthesis and cell cycle progression [39, 40]. Ran directly interacts with survivin in a cell cycle dependent manner . RING-box protein 1, a component of the SCF (SKP1-CUL1-F-box protein) and the CBC (VHL) (CUL2-elonging BC-VHL) E3 ubiquitin ligase complexes mediates ubiquitination and subsequent proteasomal degradation of target proteins involved in cell cycle progression [41, 42]. Thioredoxin-like protein 4B (Dim1), which is required for cell cycle progression at the S/G(2) transition and is associated to proteins involved in pre-mRNA splicing [43, 44]. DEAD/H box protein 11 (Chlr1), a DNA helicase required for sister chromatid cohesion . Chlr1 is essential for embryonic development and the prevention of aneuploidy . S-phase kinase-associated protein 1, an essential component of the SCF (SKP1-CUL1-F-box protein) ubiquitin ligase complex , which mediates the ubiquitination of proteins involved in cell cycle progression. Fission yeast skp1 is required for spindle morphology and nuclear membrane segregation at anaphase . Aurora kinase A-interacting protein (AKIP), a negative regulator of Aurora-A kinase , that modulates the stability of its interaction partner Aurora-A kinase in conjunction with GSK-3beta , as well as Centrin 2 and 3, involved in centrosome reproduction [51, 52]. Additionally, we selected transcripts assigned to negative regulation of apoptosis. Translational-controlled tumor protein (TCTP1, Fortilin), a multifunctional protein with potent antiapoptotic capabilities  that is essential for mouse embryonic development . Secreted frizzled-related protein 1 (SFRP1), which functions as a modulator of Wnt signaling through direct interaction with Wnts . Ring finger protein 7 (RNF7, RBX2, SAG) a component of the SCF (SKP1-CUL1-F-box protein) E3 ubiquitin ligase complex, which mediates ubiquitination and subsequent proteasomal degradation of target proteins involved in cell cycle progression, signal transduction and transcription . High mobility group box protein B1, which functions as an intracellular regulator of transcription and is also released as a cytokine by necrotic and inflammatory cells . HMGB-1 is implicated to be critically involved in skeletal muscle , as well as cardiac regeneration . Cofilin 2, an actin depolymerizing protein, implicated in congenital Nemaline myopathy (NM) . PP1beta, potentially involved in cell cycle control and apoptosis  and YWHAZ (14-3-3- zeta), which possibly modulates reorganization of the actin cytoskeleton via binding to cofilin and LIMK1 . From a third group of transcripts functionally annotated to muscle development, we chose Muscle LIM protein (CSRP3), which is critically involved in cardiomyocyte architecture, as revealed by targeted knockout . Muscle LIM protein acts as a stress sensor, linked to calcineurin-NFAT signaling at the sarcomeric Z-disk  and Interferon-related developmental regulator 1 (IFRD1), a multifunctional transcriptional regulator, that is directly interacting with p53, regulating its function  and is also required for myoblast differentiation .
We have described the generation of a large EST dataset from regenerating newt hearts. This will facilitate the molecular analysis of heart regeneration. Together with existing EST data from other urodeles such as Amybystoma mexicanum, our EST dataset might also help to uncover general principles of tissue regeneration in urodele amphibians. Careful analysis of our data allowed insights into the transcriptional program that governs heart regeneration in newts and revealed multiple changes in different biological processes ranging from the regulation of programmed cell death to cell cycle regulation and wound repair. Comparison to sequences, which are deregulated during zebrafish heart regeneration, revealed similarities but also differences during cardiac regeneration in both species.
The fundamental differences between mammals and newts in tissue regeneration argue for involvement of multiple mechanisms directing complex processes ranging from wound repair, regulation of the extracellular matrix, and cell cycle control, to proliferation and differentiation, which need to be precisely controlled to allow successful organ regeneration. Only integrated approaches that take advantage of the analysis of large data sets will eventually lead to a thorough understanding of tissue regeneration. An important part of such analysis is the comparison between organisms capable of comprehensive organ regeneration and those, which have lost (or suppress) this ability. So far the mammalian heart has proven to be resistant to tissue regeneration despite numerous attempts to rebuild lost tissue including cell transplantation and activation of potential regenerative pathways. It seems reasonable to assume that analysis of transcriptional changes, which occur in organisms capable of regeneration, might disclose the critical processes that are indispensable for the replacement of functional heart tissue. We reason that in-depth knowledge of differences that occur in response to tissue damage between regenerating and non-regenerating organisms might pave the way to manipulate and improve regenerative responses. Eventually, evolutionarily imposed restrictions to cardiac regeneration might be lifted, in particular if such restrictions are species-specific. The acquisition of sequence data, which reflect transcriptional changes during newt heart regeneration, is a first step in this direction.
Animal housing and surgery
All newts were obtained from Charles D. Sullivan newt farm, Nashville Tennessee. Adult animals were kept in aerated fresh water aquaria at constant temperature between 18-20°C and were fed twice a week with artemia larvae. For surgery, newts were deeply anaesthetized with 0.1% by Ethyl-3-aminobenzoate-methanesulfonic acid salt solved in water. Skin and body wall were opened by an incision; hearts were gently pulled out, and fixed with fine forceps at the ventricle. Injury was done be squeezing the ventricle for 15 times each in two directions, avoiding extensive bleeding. After repositioning of hearts, incisions were closed with Histoacryl (B. Braun Aesculap AG, Germany). Newts were kept for several hours in 0.5% sulfamerazine solution to avoid infections. Operated newts were kept in separate aquaria under constant observation. To collect heart samples, animals were deeply anesthetized decapitated and hearts were immediately removed and flash frozen in liquid nitrogen.
cDNA library construction, sequencing and EST contig assembly
Total RNA was isolated using Trizol according to the manufacturers instructions (Invitrogen). To construct the cDNA library 1 μg of polyA+ was purified from 30 newt hearts and subjected to the Creator SMART kit protocol as described by the manufacturer (Clontech). Briefly, 1 μg of polyA+ RNA was used to synthesize first strand cDNA. Double stranded cDNA was synthesized by primer extension, digested with SfiI, size fractionated and ligated into pDNRLib vector (Clontech). Clones were transformed into E. coli, plated onto LB/Agar plates, containing 25 μg/ml Chloramphenicol and arrayed into 384 well plates (RZPD, Berlin, Germany). Sequencing of 11520 EST clones was performed at the Max-Planck-Institute of Molecular Cell Biology, Dresden. Trace data of minor quality were removed and sequences were clipped from contaminating vector information and subsequently from 3' polyA tails. Inserts below 100 bp in length, and clones, not containing cDNA inserts were subtracted and discarded. The resulting set of 9698 EST sequences served as an input for all further analysis. Assembly was performed with the commercial software SeqMan™ Pro (DNASTAR, Lasergene) using default parameters.
BLAST search, functional annotation, EST submission and identification of enriched GO terms
BLAST searches for 2894 contigs were performed on NCBI nr and EST databases with blastn, blastx and tblastx algorithms. BLAST output files were parsed with a cut-off E-value = e-05. For each contig, 50 top scoring BLAST hits were stored in the database. Functional annotation to Gene Ontology terms was performed via BLASTx searches on TrEMBL and Swiss-Prot databases with an e-value cut-off < e-20 or Abagyan score higher than sigma = 8 . Protein identifiers were derived for 50 top scoring hits, independent of organism according to the order of BLAST hit ranking. Identifiers were used to attach GO terms from the GOA database. For contigs with no assignable similarity (e-value > 1-05), potential open reading frames and 3'untranslated regions were searched with public available software ESTscan . Annotation to GenBank was performed by splitting 2894 contigs into their respective ESTs, that were attached with the information of top ranking hits (score ≤ e-05) from all blast algorithms performed. We defined an e-value smaller than 1e-20 as "significant similarity" and an e-value between 1e-20 and e-05 as "weak similarity.
The total number of annotated proteins for newt and zebrafish datasets and all proteins within the GOA database (as of October 2008) were calculated for selected GO terms and used to determine the ratio of the total number of annotated proteins within the GO term "biological process" for all three databases used. The degree of enrichment against the GOA dataset was expressed as fold accumulation.
Annotation of deregulated Zebrafish Affy IDs
Deregulated zebrafish EST clones were selected from the Affymetrix™ zebrafish microarray sequence data file . Corresponding sequences were assembled using the commercial SeqMan™ Pro software to yield 632 contigs. Contig annotation was done using the blastx, blastn and tblastx algorithms against NCBI nr protein, nr nucleotide and EST databases using a similarity cut-off < 1e-20 (= "significant similarity") and a cut-off ≤ 1e-05 (= "weak similarity"). Assignment of GO terms was performed in the same manner as with newt contigs using blastx searches against Swiss-Prot and TrEMBL protein databases. BLAST searches against the newt EST database were performed using BLASTn with all 662 deregulated zebrafish ESTs with an e-value cut-off smaller than 1e-05.
For each time point analyzed (uninjured heart, 4, 7, 14, and 21 days after injury), 3 biological samples, pooled from 4 individual newt hearts each, were used for RNA isolation with Trizol reagent (Invitrogen). 1 μg of total RNA was reverse transcribed with Superscript II™ (Invitrogen) in a total volume of 50 μl. Oligonucleotides for all 30 candidate genes are listed in additional file 7. PCR was performed with Taq-Polymerase in 25 μl reactions at 55°C for 25-35 cycles. PCR products were visualized by agarose gel electrophoresis. Band intensities were analyzed densitometrically with Image J Version 1.40 g and used to calculate mRNA expression levels.
Data storage in a web based relational database
All information was stored in a relational database (MySQL) to facilitate easy access to the data via a web browser independent PhP frontend. The EST database was constructed as an organism unspecific relational database using the open source software MySQL. The web-based frontend was realized in PHP, database update pipelines were programmed in Perl. The open source software "apache" was used for web server services. The design of the database allows storing of newt sequence data, such as EST sequences, their respective plate coordinates, assembly information, and all annotation data derived from NCBI BLAST searches in nr and EST databases with its associated alignment and e-value score (Figure S4a in Additional file 8). It is possible to search for cDNA library plate coordinates via the frontend as well as for assembled contigs by contig number, assigned accession, or description (data not shown). Moreover, similar proteins derived from TrEMBL and Swiss-Prot databases as well as resulting functional annotation of these proteins from the GOA database were stored together with the e-value and abagyan score and can be searched via the frontend (Figure S4b in Additional file 8). All stored identifiers are directly linked to external public access databases for advanced analysis. To allow coherent GO term analysis the entire GO term tree was deposited and is browseable via the frontend, which displays the degree of direct and indirect enrichment in single GO nodes (figure S4c in Additional file 8). Further GO term visualization was implemented by links to external GO term viewers (EMBL-EBI QuickGO and SRS). The GO terms are accessible with search forms for GO term identifiers, GO term descriptions and protein descriptions. The database provides changeover between GO term-, assigned proteins- and assembled contigs-view (data not shown). The frontend does also house a BLAST search form, which allows BLASTN and BLASTX searches within the EST database for analysis of additional sequences (Supplementary figure S4d in Additional file 8). We also included an update form to facilitate addition of new sequences with corresponding contigs and annotation data. The database can be used as an annotation viewer for external software like Acuity (Axon Instruments) by a PhP based interface. The database scheme is independent of organisms. Based on a set of sequences, a pipeline of scripts generates all required data and formats relational tables. For the comparison with deregulated zebrafish genes, we also generated a zebrafish database. An updated version of the web-based frontend of the newt EST database will be publically available as of December 2009.
This work was supported by the Max-Planck-Society, the DFG (Br1416), the EU Commission (MYORES network of excellence), the Kerckhoff-Foundation and the Excellence Cluster Cardiopulmonary System. The authors declare that they have no conflicting commercial interests related to this work. We thank Franz Cemic, from the Fachhochschule Friedberg, Germany for his helpful discussion in statistical analysis.
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